Introducing HyperSense, our co-designed hardware and software system efficiently controls Analog-to-Digital Converter (ADC) modules' data generation rate based on object presence predictions in sensor data. Addressing challenges posed by escalating sensor quantities and data rates, HyperSense reduces redundant digital data using energy-efficient low-precision ADC, diminishing machine learning system costs. Leveraging neurally-inspired HyperDimensional Computing (HDC), HyperSense analyzes real-time raw low-precision sensor data, offering advantages in handling noise, memory-centricity, and real-time learning. Our proposed HyperSense model combines high-performance software for object detection with real-time hardware prediction, introducing the novel concept of Intelligent Sensor Control. Comprehensive software and hardware evaluations demonstrate our solution's superior performance, evidenced by the highest Area Under the Curve (AUC) and sharpest Receiver Operating Characteristic (ROC) curve among lightweight models. Hardware-wise, our FPGA-based domain-specific accelerator tailored for HyperSense achieves a 5.6x speedup compared to YOLOv4 on NVIDIA Jetson Orin while showing up to 92.1% energy saving compared to the conventional system. These results underscore HyperSense's effectiveness and efficiency, positioning it as a promising solution for intelligent sensing and real-time data processing across diverse applications.

翻译：本文介绍HyperSense——一种软硬件协同设计的系统，可通过传感器数据中物体存在性预测，高效控制模数转换器（ADC）模块的数据生成速率。针对传感器数量激增与数据速率攀升带来的挑战，HyperSense采用高能效的低精度ADC减少冗余数字数据，从而降低机器学习系统成本。受神经启发的超维计算（HDC）技术使HyperSense能够分析实时原始低精度传感器数据，在噪声处理、内存中心性与实时学习方面具有优势。所提出的HyperSense模型将面向目标检测的高性能软件与实时硬件预测相结合，首次提出"智能传感器控制"概念。全面的软硬件评估表明，该方案在轻量模型中具有最优性能——曲线下面积（AUC）最高且接收者操作特征（ROC）曲线最陡峭。硬件方面，基于FPGA的HyperSense专用加速器相比NVIDIA Jetson Orin平台上的YOLOv4获得5.6倍加速，同时相较传统系统实现最高92.1%的能耗节省。这些结果充分证明了HyperSense的高效性与有效性，使其成为面向智能感知与实时数据处理的跨领域解决方案。